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Paper Citation Record · LEDGER

1 bit is all we need: binary normalized neural networks

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2509.07025.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.07025 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T04:37:12.933596Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

  • verified exact2
  • verified fuzzy11
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0b12366-daf6-44d3-b2b8-693d34ae4e20 · outbound

This paper cites Deep Learning.

1 bit is all we need: binary normalized neural networks Deep Learning

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.421058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.856357Z digest=sha256:70b261be8c0fe62b96eb090c1f6c38fd46a75c0c601ade06736170b5e1122b39

Observation 1c1b5c19-52f3-4cbf-bcc5-cfcfeeb1b027 · outbound

This paper cites Henzinger, Mathias Lechner, and Dj\'or d e Z ikeli \' c.

1 bit is all we need: binary normalized neural networks Henzinger, Mathias Lechner, and Dj\'or d e Z ikeli \' c

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.408775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.860795Z digest=sha256:adf3eff5e0347e8a202729202ba7c1dc6e17a0320110473ebee4f1ddba7000b5

Observation ce4034de-2958-4327-9d6a-21fa076c8e33 · outbound

This paper cites Edge intelligence: Challenges and opportunities of near-sensor machine learning applications.

1 bit is all we need: binary normalized neural networks Edge intelligence: Challenges and opportunities of near-sensor machine learning applications

Reference 3

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T04:37:13.260257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.864782Z digest=sha256:b43c7d4485a0ebafce2ba44a0bfbef1a93b2903db1586041df44caddb69b0850

Observation 667a9965-acc7-4789-81d5-f6c9bc16da80 · outbound

This paper cites an unresolved cited work.

1 bit is all we need: binary normalized neural networks Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-05T04:37:13.396472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.868696Z digest=sha256:4f911d0c85612270bebd488ad5b06906201900ef35f47f58f264480376c52139

Observation db2de27d-a0b0-4791-b05b-26ccdf45c915 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

1 bit is all we need: binary normalized neural networks Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T04:37:12.873073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:37:12.873073Z digest=sha256:578af7920a8761b1ab415fd2438338739fb89c54e29a4385f34d0cb48c931061

Observation a25031ac-1e46-4082-a8f3-48adbceb3562 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

1 bit is all we need: binary normalized neural networks Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T04:37:12.877931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:37:12.877931Z digest=sha256:c0174e3c51872f30a805c44dcb27f7af15219d7c16673e62022fef78fd3a8899

Observation 152df341-b140-447d-94e9-8f684c9cec83 · outbound

This paper cites Post-training 4-bit quantization of convolutional networks for rapid-deployment.

1 bit is all we need: binary normalized neural networks Post-training 4-bit quantization of convolutional networks for rapid-deployment

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.383763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.882793Z digest=sha256:24a429eca1f7d0fc2fef3d62eddacfb55129ede0d6bf233212234939e104edc4

Observation 8dca99d4-5f75-41f5-b54d-05139a65082c · outbound

This paper cites Quantized neural networks: Training neural networks with low precision weights and activations.

1 bit is all we need: binary normalized neural networks Quantized neural networks: Training neural networks with low precision weights and activations

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.370448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.886699Z digest=sha256:f90afb1ab17a9c656e39bb6fe54b90f8e984a7712af59f708eef2fe87dd18b20

Observation 26ffd4a1-8135-4d70-8f66-f8f90bcb6057 · outbound

This paper cites Neural Discrete Representation Learning.

1 bit is all we need: binary normalized neural networks Neural Discrete Representation Learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T04:37:12.890455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:37:12.890455Z digest=sha256:47c12e3bd3e3e1a79ef06fa4505a9f7548d2c694bff758f2278a7005f1077c01

Observation 0d49ffdc-3fdb-4ba9-864b-f876fddce495 · outbound

This paper cites Accurate and efficient 2-bit quantized neural networks.

1 bit is all we need: binary normalized neural networks Accurate and efficient 2-bit quantized neural networks

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.356928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.894603Z digest=sha256:07ac4f02347e10a131b4bab5c873457257c23bdd45f1624eb358ca22f1fc685c

Observation 2748e10f-778d-4db3-b4e5-193169331457 · outbound

This paper cites Zhuang, C.

1 bit is all we need: binary normalized neural networks Zhuang, C

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.342772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.898299Z digest=sha256:2a2eabe068c7189aa73a8dbe630bc57a6d0da72dbe4a1dd4d936a71472e67130

Observation a4755772-be89-47ba-8ad8-1ae3b982bfcf · outbound

This paper cites Neural networks with low-resolution parameters.

1 bit is all we need: binary normalized neural networks Neural networks with low-resolution parameters

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.328777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.902075Z digest=sha256:d0d9b8880c25b6ed2375e71bd576530743da8f3cb48ab7991915f9af3ae6db95

Observation 93e9cdec-360d-4206-952d-88744c5dd494 · outbound

This paper cites Fixed-point feedforward deep neural network design using weights +1, 0, and -1.

1 bit is all we need: binary normalized neural networks Fixed-point feedforward deep neural network design using weights +1, 0, and -1

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.313920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.906402Z digest=sha256:ac7423ce2f24c90e810ca76cfb8df06d0716517e6aec902b83c03dcba72cbc27

Observation da392300-8cd7-42c1-8034-2552448a1605 · outbound

This paper cites Binaryconnect: Training deep neural networks with binary weights during propagations.

1 bit is all we need: binary normalized neural networks Binaryconnect: Training deep neural networks with binary weights during propagations

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.300919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.910613Z digest=sha256:4e6cfedb262cfd271719517474714b6d0886d56c5d4725f702465d20eed02f05

Observation 64fea970-f898-4f37-b487-fb1f8e7a2a12 · outbound

This paper cites Binarized neural networks.

1 bit is all we need: binary normalized neural networks Binarized neural networks

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.286882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.914462Z digest=sha256:236ef42d1fe0128dfa8b0d162cb34a98f751569026b353d6ceab31b4d3bd2126

Observation 0a5ef66b-b7be-42d6-a4fd-40d9676ffa46 · outbound

This paper cites Xnor-net: Imagenet classification using binary convolutional neural networks.

1 bit is all we need: binary normalized neural networks Xnor-net: Imagenet classification using binary convolutional neural networks

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T04:37:12.918357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:37:12.918357Z digest=sha256:6e907dc75f5c3059fcc4bcd58d443499548f7290108c1ee89042bcc3f8c665a6

Observation 131d1bfc-399f-4504-9d3e-b76bd042c105 · outbound

This paper cites an unresolved cited work.

1 bit is all we need: binary normalized neural networks Unresolved cited work

Reference 17

Resolution
verified exact
raw_fallback, observed 2026-08-05T04:37:13.145038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.922188Z digest=sha256:07b69d939d07dbc523873925eb402000d133e3a60b6583888fe0c2b4a57a1079

Observation d32c1b61-7f44-403c-9ad8-55cdb51f1e72 · outbound

This paper cites Food-101 – mining discriminative components with random forests.

1 bit is all we need: binary normalized neural networks Food-101 – mining discriminative components with random forests

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T04:37:13.273505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.925857Z digest=sha256:d2a4386bd8333321151a2c83fbc0ba8a90eabe7e7927d68175bbd0918e909cb1

Observation a328b808-7676-4bb5-add9-5be85894adce · outbound

This paper cites Pointer Sentinel Mixture Models.

1 bit is all we need: binary normalized neural networks Pointer Sentinel Mixture Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T04:37:12.929552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:37:12.929552Z digest=sha256:da91b4d903d3f1b77c575c33922e1fc7ae8ad2e62acf0721364be719bfd4e2f2

Observation 2754aa16-85e2-46d1-86d2-88bbb3282973 · outbound

This paper cites Fast WordPiece Tokenization.

1 bit is all we need: binary normalized neural networks Fast WordPiece Tokenization

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-08-05T04:37:12.997768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-05T04:37:12.933596Z digest=sha256:69b694af9467293efa539744d41b4841f4db706046204fc671aa4ce649b838c6

Pith citing papers

No inbound Pith citation observations are available.